Self-limiting temperature electric tracing band heating current monitoring method and system

Abstract

The present application relates to the field of current monitoring, in particular to a self-limiting temperature electric tracing band heating current monitoring method and system. The method first acquires current data, surface temperature data and ambient temperature data of the electric tracing band, constructs a sliding window for each time, wavelet decomposes the current data in the sliding window of each time, obtains current component data of the sliding window of each time, clusters the current component data according to the difference between the current component data and the surface temperature data of the sliding window of each time and the difference between the current component data and the ambient temperature data, obtains a first cluster and a second cluster, according to the distribution of each current component data of the sliding window of the current time in the first cluster and the second cluster and the signal-to-noise ratio of the current data, obtains a current fault coefficient of the current time, and performs real-time fault monitoring on the current of the electric tracing band. The present application can accurately monitor the current fault of the self-limiting temperature electric tracing band.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of current monitoring, in particular to a self-limiting temperature electric tracing band heating current monitoring method and system. BACKGROUND

[0002] The self-limiting temperature electric tracing band is an electric heating device based on PTC material. When the ambient temperature decreases, the PTC material resistance decreases, the current increases, and the heating power increases. Conversely, the increase of ambient temperature leads to the increase of resistance, which limits the increase of current and reduces the heating power, realizing the "self-limiting temperature" characteristic. This characteristic makes it have high safety, but at the same time, it also challenges the real-time stability of the current.

[0003] Because the ambient temperature will affect the self-limiting temperature electric tracing band, when the ambient temperature is too low, the self-limiting temperature electric tracing band has PTC material characteristics, so that its own resistance is low, resulting in strong heating current when powered on, and when the ambient temperature is too high, it will make its resistance higher, resulting in weak heating current when powered on, resulting in the current of the self-limiting temperature electric tracing band changing with temperature, which will cover current overload, short circuit and open circuit and other current faults, thereby reducing the accuracy of monitoring the current faults of the self-limiting temperature electric tracing band. SUMMARY

[0004] In order to solve the technical problem that the current of the self-limiting temperature electric tracing band changes with temperature, which will cover current overload, short circuit and open circuit and other current faults, thereby reducing the accuracy of monitoring the current faults of the self-limiting temperature electric tracing band, the purpose of the present application is to provide a self-limiting temperature electric tracing band heating current monitoring method and system, and the technical solution adopted is as follows:

[0005] The present application provides a self-limiting temperature electric tracing band heating current monitoring method, which comprises:

[0006] Obtaining current data, surface temperature data and ambient temperature data of the electric tracing band during working process;

[0007] According to the sampling frequency of the current data and the local distribution of the current data at each moment, a sliding window at each moment is constructed. According to the fluctuation of the current data in the sliding window at each moment and the signal-to-noise ratio, the current data in the sliding window at each moment is wavelet decomposed to obtain the current component data of the sliding window at each moment;

[0008] Clustering the current component data in the sliding windows at all moments based on the difference between the current component data and the surface temperature data, and the difference between the current component data and the ambient temperature data, to obtain a plurality of first clusters and a plurality of second clusters; obtaining the degree of current influence of the sliding window at the current moment based on the distribution of each current component data in the sliding window at the current moment in the first clusters and the second clusters; obtaining the current fault coefficient at the current moment based on the signal-to-noise ratio of the current data in the sliding window at the current moment and the degree of current influence;

[0009] Based on the current fault coefficient at the current moment, real-time fault monitoring of the current of the electric heating tape is performed.

[0010] Furthermore, constructing the sliding window at each moment includes:

[0011] Rounding the product of the preset first coefficient and the sampling frequency of the current data to obtain the basic length at each moment;

[0012] Taking the average value of the current data at all moments in the basic window at each moment as the local current level at each moment, wherein the length of the basic window is equal to the basic length;

[0013] The product value of the preset second coefficient and the rated current of the electric heating tape is used as the current judgment threshold, and when the local current level at each moment is not greater than the current judgment threshold, the basic window at each moment is used as the sliding window at each moment;

[0014] When the local current level at each moment is greater than the current judgment threshold, the length of the basic window at each moment is reduced to obtain a sliding window at each moment.

[0015] Furthermore, obtaining the current component data of the sliding window at each moment includes:

[0016] According to the fluctuation of the current data in the sliding window at each moment, a wavelet basis is selected for the sliding window at each moment;

[0017] According to the signal-to-noise ratio of the current data of the sliding window at each moment, the number of decomposition layers of the sliding window at each moment is obtained;

[0018] Based on the wavelet basis and the decomposition layer number of the sliding window at each moment, the current data in the sliding window at each moment is subjected to wavelet decomposition, and the low-frequency components and high-frequency components obtained by the decomposition are used as the current component data of the sliding window at each moment.

[0019] Furthermore, selecting a wavelet basis from the sliding window at each moment includes:

[0020] Performing Fourier transform on the current data in the sliding window at each moment to obtain multiple frequency components in the sliding window at each moment, screening out high-frequency components from all frequency components in the sliding window at each moment, and obtaining high-frequency component energy of the sliding window at each moment based on all the high-frequency components in the sliding window at each moment using Parseval's theorem;

[0021] Take any moment as the target moment, and based on the calculation formula of the wavelet selection factor, obtain the wavelet selection factor of the sliding window at the target moment. The calculation formula of the wavelet selection factor is:

[0022]

[0023]

[0024] in, The wavelet selection factor representing the sliding window of the target time; represents the kurtosis of the current data in the sliding window at the target time; represents the standard deviation of the current data in the sliding window at the target time; Relative coefficient of change of the sliding window representing the target moment; Represents the high-frequency component energy of the sliding window at the target moment; The center point of the sliding window representing the target time and the The distance between the center points of the sliding windows at other moments; Indicates the time other than the target time The high-frequency component energy of the sliding window at other moments; Expressed as a natural constant An exponential function with base ; represents the normalization function; Indicates preset adjustment parameters;

[0025] If the wavelet selection factor of the sliding window at the target moment is greater than the preset selection threshold, the Haar wavelet is selected as the wavelet basis of the sliding window at the target moment; if the wavelet selection factor of the sliding window at the target moment is not greater than the preset selection threshold, the Symlet wavelet is selected as the wavelet basis of the sliding window at the target moment.

[0026] Furthermore, obtaining the number of decomposition layers of the sliding window at each moment includes:

[0027] Use support vector machine to divide the signal-to-noise ratio of the current data of the sliding window at all times into two categories, and take the average of all signal-to-noise ratios in each category as the overall signal-to-noise ratio of each category;

[0028] a preset first layer number is set to the decomposition layers of the sliding window corresponding to each time point in the category of the maximum value of the overall signal-to-noise ratio;

[0029] a preset second layer number is set to the decomposition layers of the sliding window corresponding to each time point in the category of the minimum value of the overall signal-to-noise ratio, wherein the preset first layer number is less than the preset second layer number.

[0030] Further, the obtaining of the plurality of first clustering clusters and the plurality of second clustering clusters comprises:

[0031] using a dynamic time warping algorithm, processing each of the current component data of each time point and the surface temperature data of each time point, to obtain a first minimum cumulative distance of each current component data in each time point;

[0032] for all current component data in all time points, taking the absolute value of the difference of the first minimum cumulative distance of any two current component data as a first distance measurement between any two current component data, and clustering all current component data in all time points to obtain a plurality of first clustering clusters;

[0033] using a dynamic time warping algorithm, processing each of the current component data of each time point and the ambient temperature data of each time point, to obtain a second minimum cumulative distance of each current component data in each time point;

[0034] for all current component data in all time points, taking the absolute value of the difference of the second minimum cumulative distance of any two current component data as a second distance measurement between any two current component data, and clustering all current component data in all time points to obtain a plurality of second clustering clusters.

[0035] Further, the obtaining of the current time point sliding window current component data affected degree comprises:

[0036] taking the average value of the first minimum cumulative distance of all current component data in the current time point sliding window as the first overall cumulative distance of the current time point sliding window;

[0037] averaging the first minimum cumulative distance of all current component data in each first clustering cluster as a first clustering center value of each first clustering cluster, taking the absolute value of the difference between the first minimum cumulative distance of any current component data in the sliding window of the current moment and the first clustering center value of the first clustering cluster where the current component data is located as a first distance parameter of the current component data, and averaging the first distance parameters of all current component data in the sliding window of the current moment as a first overall distance parameter of the sliding window of the current moment;

[0038] averaging the second minimum cumulative distance of all current component data in the sliding window of the current moment as a second overall cumulative distance of the sliding window of the current moment;

[0039] averaging the second minimum cumulative distance of all current component data in each second clustering cluster as a second clustering center value of each second clustering cluster, taking the absolute value of the difference between the second minimum cumulative distance of any current component data in the sliding window of the current moment and the second clustering center value of the second clustering cluster where the current component data is located as a second distance parameter of the current component data, and averaging the second distance parameters of all current component data in the sliding window of the current moment as a second overall distance parameter of the sliding window of the current moment;

[0040] obtaining the current moment current affected degree of the sliding window based on the current affected degree calculation formula, wherein the current affected degree calculation formula is:

[0041]

[0042] wherein, represents the current moment current affected degree of the sliding window; represents the first overall cumulative distance of the sliding window of the current moment; represents the first overall distance parameter of the sliding window of the current moment; represents the second overall cumulative distance of the sliding window of the current moment; represents the second overall distance parameter of the sliding window of the current moment.

[0043] Further, the obtaining of the current moment current fault coefficient includes:

[0044] performing negative correlation mapping on the signal-to-noise ratio of the current moment current data of the sliding window to obtain the current moment current abnormality degree of the sliding window;

[0045] The current fault coefficient of the current moment is obtained by synthesizing and normalizing the current abnormality degree and the current affected degree of the current moment.

[0046] Further, the real-time fault monitoring of the current of the electric tracing band comprises:

[0047] If the current fault coefficient of the current moment is not greater than a preset first fault threshold, the current fault type of the current moment is set as a normal fluctuation type, if the current fault coefficient of the current moment is greater than the preset first fault threshold and not greater than a preset second fault threshold, the current fault type of the current moment is set as a current overload type, and if the current fault coefficient of the current moment is greater than the preset second fault threshold, the current fault type of the current moment is set as a short circuit or an open circuit.

[0048] When any one of the current fault types is monitored at a single time, a log is recorded, when the current overload type or the short circuit or the open circuit is monitored continuously for multiple times, an alarm information is triggered, and when the short circuit or the open circuit is monitored continuously for more than a preset time length, the power supply is cut off.

[0049] The application further provides a self-limiting temperature electric tracing band heating current monitoring system, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of any one of the self-limiting temperature electric tracing band heating current monitoring methods when executing the computer program.

[0050] The application has the following beneficial effects:

[0051] The application considers that the current of the self-limiting temperature electric tracing band changes with the temperature, which can cover current faults such as current overload, short circuit and open circuit, thereby reducing the accuracy of monitoring the current faults of the self-limiting temperature electric tracing band, and the application combines current data, surface temperature data of the electric tracing band and environmental temperature data for comprehensive analysis, which can effectively avoid the current fault misidentification problem caused by the mutual relationship between the current and the temperature, the current signal is processed through dynamic wavelet decomposition, the current characteristics of different stages can be accurately extracted, misidentification in low-temperature or high-temperature environments can be avoided, in addition, the wavelet decomposition process of the current signal is adjusted in real time by combining the transient characteristics of the current signal, the changes of the current data of different working stages can be adaptively adjusted, more subtle abnormal signals can be captured, and the changes of the current signal of the self-limiting temperature electric tracing band in different working stages can be adapted, effective monitoring of various stages is ensured, and the accuracy of current fault identification in the dynamic changes of the current data is improved. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, below will briefly introduce the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A self-limiting temperature electric tracing band heating current monitoring method flow chart provided by an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purposes, the following describes in detail the specific embodiments, structure, features and effects of the self-limiting temperature electric tracing band heating current monitoring method and system according to the present application, with reference to the drawings and preferred embodiments. Different "one embodiment" or "another embodiment" in the following description do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0056] The following specifically describes the specific scheme of the self-limiting temperature electric tracing band heating current monitoring method and system provided by the present application with reference to the drawings.

[0057] Please refer to Figure 1 which shows a self-limiting temperature electric tracing band heating current monitoring method flow chart provided by an embodiment of the present application. The method comprises:

[0058] Step S1: obtaining current data, surface temperature data and ambient temperature data of the electric tracing band in the working process;

[0059] The self-limiting temperature electric tracing band is usually used in some cases with stability requirements for ambient temperature. In these scenarios, the temperature of the environment where the self-limiting temperature electric tracing band is located usually has certain variation characteristics. Due to the PTC material characteristics of the self-limiting temperature electric tracing band, it is affected by the ambient temperature, so that when the ambient temperature is low, the current path is increased, so that when it is powered in a low-temperature environment, the instantaneous current will be higher, which may be misrecognized as a short circuit or overload condition in current fault detection. In a high-temperature environment, the current path is greatly reduced, so that the current in the electric tracing band is weak, which may mask the open circuit fault in current fault detection.

[0060] Therefore, in order to more accurately monitor the heating current of the self-limiting temperature electric tracing band, the temperature of the self-limiting temperature electric tracing band and the ambient temperature need to be analyzed, so the embodiment of the present application firstly connects a high-precision current sensor (such as a Hall sensor) in series in the power supply circuit of the self-limiting temperature electric tracing band, sets the sampling frequency to be greater than 1 kHz, and uses the current sensor to collect the current data of the electric tracing band during the working process.

[0061] At the same time, a temperature sensor (such as an NTC temperature sensor) is arranged on the surface of the electric tracing band, the sampling frequency is set to be greater than 10 Hz, and the temperature sensor is used to collect the surface temperature data of the electric tracing band during the working process, and a temperature sensor (such as a DS18B20) is installed around the electric tracing band, and the temperature sensor is used to collect the ambient temperature data of the electric tracing band during the working process.

[0062] At this point, the current data, surface temperature data and ambient temperature data of the electric tracing band during the working process have been collected.

[0063] Step S2: According to the sampling frequency of the current data and the local distribution of the current data at each moment, a sliding window at each moment is constructed; according to the fluctuation of the current data in the sliding window at each moment and the signal-to-noise ratio, the current data in the sliding window at each moment is wavelet decomposed to obtain the current component data of the sliding window at each moment.

[0064] The surface temperature, resistance and heating current of the self-limiting temperature electric tracing band have a mutual influence relationship, the current increases, the surface temperature of the electric tracing band rises, then the resistance in the electric tracing band rises, which causes the current to start to decrease, then the temperature of the electric tracing band decreases, then the resistance decreases, the current increases, and the cycle is repeated to realize temperature self-limiting. During the power-on process of the self-limiting temperature electric tracing band, the current data mainly includes the current in the starting stage and the current in the steady-state working stage, the starting stage current of the self-limiting temperature electric tracing band is usually several times higher than the steady-state working stage current, and the steady-state working current will fluctuate to a certain extent with the change of the resistance of the self-limiting temperature electric tracing band. Therefore, the current data of the self-limiting temperature electric tracing band has different change characteristics in different stages, so the current data needs to be analyzed in stages, so as to select a suitable wavelet basis and decomposition level to decompose and process the current data.

[0065] Since the current data of the self-limiting temperature electric tracing band has dynamic stage characteristics, when in the starting stage, the corresponding current amplitude will suddenly rise, and the duration is short, and when in the steady state stage, the periodicity of the corresponding current signal is affected by the self-limiting temperature regulation and exists small fluctuations, in order to consider the characteristics of different stages, the length of the sliding window should meet the following requirements: in the starting stage, the window should be short enough to capture transient mutations, and in the steady state stage, the window should be long enough to suppress random noise and extract low-frequency trends, therefore, the embodiment of the present application first constructs a sliding window at each time according to the sampling frequency of the current data and the local distribution of the current data at each time, and then the current data in the sliding window at each time can be decomposed by wavelet, thereby improving the feature capture effect of the current data in different stages.

[0066] Preferably, in an embodiment of the present application, the method for obtaining the sliding window at each time specifically comprises:

[0067] Firstly, the product value of the preset first coefficient and the sampling frequency of the current data is rounded to obtain the base length at each time, wherein in order to make the window constructed by the base length cover the typical fluctuation period of the current data in the steady state stage, the value range of the preset first coefficient is usually set to In an embodiment of the present application, the preset first coefficient is set to 0.2, and the specific value of the preset first coefficient can also be set by the implementer according to the specific implementation scene, which is not limited here.

[0068] Then, the average value of the current data at each time in the base window at each time is taken as the local current level at each time, wherein the length of the base window is equal to the base length, that is, for any time, the base window at the time contains the time and the nearest times to the time, and the number of times contained in the base window is equal to the base length.

[0069] The product value of the preset second coefficient and the rated current of the electric tracing band is taken as the current judgment threshold, when the local current level at each time is not greater than the current judgment threshold, it indicates that the local current data at the time is in the steady state working stage, at this time, the sliding window at the time should be long enough, therefore, the base window at each time can be taken as the sliding window at each time, wherein the value range of the preset second coefficient is In an embodiment of the present application, the preset second coefficient is set to 1.5, and the specific value of the preset second coefficient can also be set by the implementer according to the specific implementation scene, which is not limited here.

[0070] When the local current level at each time is greater than the current judgment threshold, it indicates that the local current data at the time is in the starting stage, at this time, the sliding window at the time should be short enough, therefore, it is necessary to reduce the length of the basic window at each time to obtain the sliding window at each time, specifically, the length of the basic window at each time can be shortened to one half or one fifth of the original, etc., which is not limited here.

[0071] Due to the complex fluctuation of current data and the difference in current fluctuation characteristics in different stages, in order to effectively analyze the components of the current data and accurately identify the signal characteristics caused by each component of the current data when a fault occurs, the embodiments of the present application first select a suitable wavelet basis and decomposition layer number according to the fluctuation and signal-to-noise ratio of the current data in the sliding window at each time, and then perform wavelet decomposition on the current data in the sliding window at each time to obtain the current component data of the sliding window at each time, which is helpful to extract multi-level features from the current data, so that the various components in the current data can be more accurately analyzed, and the different components in the current data can be combined with the heat tracing band temperature data and the environmental temperature data for analysis in the subsequent process, so as to determine the relationship between the current data and the surface temperature data and the environmental temperature data when a fault exists in the current data, thereby realizing accurate monitoring of the current fault of the self-limiting temperature electric heat tracing band.

[0072] Preferably, in an embodiment of the present application, the method for obtaining the current component data of the sliding window at each time specifically comprises:

[0073] Firstly, since the fluctuation characteristics of the current data in the sliding window at different times are different, selecting a suitable wavelet basis is helpful to accurately extract different frequency components of the current signal and improve the capture ability of fault characteristics, therefore, it is necessary to select a wavelet basis for each sliding window at each time according to the fluctuation of the current data in the sliding window at each time.

[0074] Preferably, in an embodiment of the present application, the method for obtaining the wavelet basis of the sliding window at each time specifically comprises:

[0075] Performing Fourier transform on the current data in each sliding window at each time to obtain a plurality of frequency components in the sliding window at each time, and screening high frequency components from all frequency components in the sliding window at each time, using the Parseval theorem, and based on all high frequency components in the sliding window at each time, obtaining the high frequency component energy of the sliding window at each time, wherein the method for calculating the high frequency component energy by using the Parseval theorem is a well-known technical means in the art, which is not described here.

[0076] Specifically, in all frequency components in the sliding window at each time, the frequency greater than The frequency components of are taken as high frequency components, where Indicates the The maximum frequency of all frequency components in the sliding window at a moment, Indicates the preset scaling factor, the value range is usually In one embodiment of the present invention, Set to 0.7, The specific value of can also be set by the implementer according to the specific implementation scenario and is not limited here.

[0077] Take any moment as the target moment, and based on the calculation formula of the wavelet selection factor, obtain the wavelet selection factor of the sliding window at the target moment. The calculation formula of the wavelet selection factor is:

[0078]

[0079]

[0080] in, The wavelet selection factor representing the sliding window of the target time; The kurtosis of the current data in the sliding window at the target time can be obtained using the existing kurtosis formula; represents the standard deviation of the current data in the sliding window at the target time; Relative coefficient of change of the sliding window representing the target moment; Represents the high-frequency component energy of the sliding window at the target moment; The center point of the sliding window representing the target time and the The distance between the center points of the sliding windows at other moments; Indicates the time other than the target time The high-frequency component energy of the sliding window at other moments; Expressed as a natural constant An exponential function with base ; Indicates the preset first adjustment parameter, which is used to prevent the denominator from being 0. The value range is In one embodiment of the present invention, Set to 0.01, The specific value of can also be set by the implementer according to the specific implementation scenario and is not limited here; represents a normalization function, and in an embodiment of the present application, the normalization can be specifically, for example, a max-min normalization, and the normalization in the subsequent steps can also be max-min normalization, and in other embodiments of the present application, other normalization methods can be selected according to the specific range of values, which will not be described here.

[0081] wherein the relative change coefficient is used to reflect the frequency characteristics of the current data in the sliding window at the target moment relative to the current data in other sliding windows, and the relative change coefficient is greater, the higher the frequency of the current data in the sliding window at the target moment relative to the current data in other sliding windows and the higher the degree of containing high-frequency components, and as the distance weight, the closer the sliding window at other moments to the sliding window at the target moment and the greater the energy of the high-frequency components, the more high-frequency components contained in the sliding window at the target moment, at the same time, the kurtosis and the standard deviation of the current data in the sliding window at the target moment are greater, the more prominent the high-frequency part of the sliding window at the target moment, and the greater the wavelet selection factor of the sliding window at the target moment.

[0082] If the wavelet selection factor of the sliding window at the target moment is greater than a preset selection threshold, it indicates that the current data of the sliding window at the target moment contains more high-frequency parts, and therefore the Haar wavelet needs to be selected as the wavelet basis of the sliding window at the target moment, so as to more accurately capture the transient changes of the current data in the sliding window at the target moment, and if the wavelet selection factor of the sliding window at the target moment is not greater than the preset selection threshold, it indicates that the current data of the sliding window at the target moment contains more low-frequency parts, and therefore the Symlet wavelet needs to be selected as the wavelet basis of the sliding window at the target moment, so as to effectively capture the low-frequency trend, wherein the preset selection threshold has a value range of In an embodiment of the present application, the preset selection threshold is set to 0.7, and the specific value of the preset selection threshold can also be set by the implementer according to the specific implementation scene, which is not limited here.

[0083] The above-mentioned same method can be used to select the corresponding wavelet basis in each sliding window at each moment.

[0084] Then, in addition to selecting the appropriate wavelet basis, dynamically adjusting the number of layers of wavelet decomposition can adapt to the changes of the current signal at different stages, so that more high-frequency details can still be captured when the signal-to-noise ratio is low, thereby improving the sensitivity of current fault detection, and therefore the decomposition layer number of each sliding window at each moment can be obtained according to the signal-to-noise ratio of the current data of each sliding window at each moment.​

[0085] Preferably, in one embodiment of the present application, the method for obtaining the decomposition layer number of each sliding window at each time point specifically comprises:

[0086] The signal-to-noise ratios of the current data of all sliding windows at all time points are divided into two categories using a support vector machine, and the average value of all signal-to-noise ratios in each category is taken as the overall signal-to-noise ratio of each category. The method for obtaining the signal-to-noise ratio is a technical means well known to those skilled in the art, and will not be described here.

[0087] For the sliding window with a low signal-to-noise ratio of current data, the number of wavelet decomposition layers needs to be increased to capture more subtle abnormal current signals. Therefore, the decomposition layer number of the sliding window at each time point corresponding to the signal-to-noise ratio in the category of the maximum overall signal-to-noise ratio is set to a preset first layer number, and the decomposition layer number of the sliding window at each time point corresponding to the signal-to-noise ratio in the category of the minimum overall signal-to-noise ratio is set to a preset second layer number, wherein the preset first layer number is less than the preset second layer number, and the value range of the preset first layer number and the preset second layer number is generally In one embodiment of the present application, the preset first layer number is set to 6, and the preset second layer number is set to 8. The specific values of the preset first layer number and the preset second layer number can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0088] Further, the current data in each sliding window at each time point can be wavelet-decomposed based on the wavelet basis and the decomposition layer number of each sliding window at each time point, and the low-frequency component and the high-frequency component obtained by the decomposition are taken as the current component data of each sliding window at each time point. The wavelet decomposition is a technical means well known to those skilled in the art, and will not be described here.

[0089] At this point, the wavelet decomposition of the current data of the sliding window at different time points is realized.

[0090] Step S3: clustering the current component data in all sliding windows at all time points according to the difference between the current component data and the surface temperature data of each sliding window at each time point, and the difference between the current component data and the ambient temperature data of each sliding window at each time point, obtaining a plurality of first clustering clusters and a plurality of second clustering clusters; obtaining the current affected degree of the current sliding window according to the distribution of each current component data of the current sliding window in the first clustering cluster and the second clustering cluster; and obtaining the current current fault coefficient according to the signal-to-noise ratio of the current data of the current sliding window and the current affected degree.

[0091] Since the current data is correlated with the surface temperature data of the electric heat tracing belt and the environmental temperature data, comprehensive analysis can be performed on the decomposed current component data and the surface temperature data of the electric heat tracing belt and the environmental temperature data. In the embodiment of the application, first, the current component data in the sliding window at each time is clustered according to the difference between the current component data and the surface temperature data in the sliding window at each time and the difference between the current component data and the environmental temperature data in the sliding window at each time, to obtain a plurality of first clustering clusters and a plurality of second clustering clusters. Subsequently, based on the distribution characteristics of the current component data in the sliding window at the current time in the first clustering cluster and the second clustering cluster, the influence of different types of temperature on the current data in the sliding window at the current time is accurately analyzed.

[0092] Preferably, in an embodiment of the application, the method for obtaining the plurality of first clustering clusters and the plurality of second clustering clusters specifically comprises:

[0093] The dynamic time warping algorithm can reflect the difference between time series by calculating the minimum cumulative distance between time series. Therefore, the dynamic time warping algorithm can be used to process each current component data in the sliding window at each time and the surface temperature data in the sliding window at each time, to obtain a first minimum cumulative distance of each current component data in the sliding window at each time. The greater the first minimum cumulative distance, the greater the difference between the current component data and the surface temperature data in the sliding window.

[0094] For all current component data in the sliding window at all times, the absolute value of the difference between the first minimum cumulative distances of any two current component data is taken as the first distance metric between any two current component data, and all current component data in the sliding window at all times are clustered to obtain a plurality of first clustering clusters. Optionally, the existing K-means clustering algorithm can be used to realize the clustering operation, and the number of clustering clusters can be determined by the existing elbow method, which will not be described here.

[0095] Similarly, the dynamic time warping algorithm is used to process each current component data in the sliding window at each time and the environmental temperature data in the sliding window at each time, to obtain a second minimum cumulative distance of each current component data in the sliding window at each time.

[0096] For all current component data in the sliding window at all times, the absolute value of the difference between the second minimum cumulative distances of any two current component data is taken as the second distance metric between any two current component data, and all current component data in the sliding window at all times are clustered to obtain a plurality of second clustering clusters. The clustering algorithms selected in the two clustering operations are the same, so as to avoid large errors in subsequent analysis.

[0097] Then the current time sliding window is analyzed, and the current time sliding window current affected degree is obtained according to the distribution of each current component data in the current time sliding window in the first clustering cluster and the second clustering cluster. The greater the current affected degree is, the greater the current data in the current time sliding window is affected by the electric heat tracing band surface temperature, and the stronger the correlation with the electric heat tracing band surface temperature is, and then the current time current fault condition can be accurately calculated and analyzed based on the current affected degree.

[0098] Preferably, in one embodiment of the present application, the current time sliding window current affected degree obtaining method specifically comprises:

[0099] The average value of the first minimum cumulative distance of all current component data in the current time sliding window is taken as the first overall cumulative distance of the current time sliding window.

[0100] The average value of the first minimum cumulative distance of all current component data in each first clustering cluster is taken as the first clustering center value of each first clustering cluster. For any one current component data in the current time sliding window, the absolute value of the difference between the first minimum cumulative distance of the current component data and the first clustering center value of the first clustering cluster where the current component data is located is taken as the first distance parameter of the current component data. The average value of the first distance parameter of all current component data in the current time sliding window is taken as the first overall distance parameter of the current time sliding window.

[0101] The average value of the second minimum cumulative distance of all current component data in the current time sliding window is taken as the second overall cumulative distance of the current time sliding window.

[0102] The average value of the second minimum cumulative distance of all current component data in each second clustering cluster is taken as the second clustering center value of each second clustering cluster. For any one current component data in the current time sliding window, the absolute value of the difference between the second minimum cumulative distance of the current component data and the second clustering center value of the second clustering cluster where the current component data is located is taken as the second distance parameter of the current component data. The average value of the second distance parameter of all current component data in the current time sliding window is taken as the second overall distance parameter of the current time sliding window.

[0103] The current time sliding window current affected degree is obtained based on the current affected degree calculation formula. The current affected degree calculation formula is:

[0104]

[0105] wherein, represents the current time of the sliding window of the current time affected by the degree of current; represents the first overall cumulative distance of the sliding window of the current time; represents the first overall distance parameter of the sliding window of the current time; represents the second overall cumulative distance of the sliding window of the current time; represents the second overall distance parameter of the sliding window of the current time.

[0106] wherein, much less than , it indicates that the correlation between the current data of the current time in the sliding window and the surface temperature data of the electric heat tracing pipe is strong, and much greater than , it indicates that the correlation between the current data of the current time in the sliding window and the ambient temperature data is weak, and at this time the current is mainly affected by the temperature of the electric heat tracing pipe itself, and the possibility of failure is greater.

[0107] The smaller the signal-to-noise ratio of the current data of the sliding window of the current time, the more abnormal the current of the current time is, and the greater the degree of current affected by the sliding window of the current time, the more likely the abnormal state of the current of the electric heat tracing pipe at this time is caused by its own temperature rather than the ambient temperature. Therefore, according to the signal-to-noise ratio of the current data of the sliding window of the current time and the degree of current affected, the current fault coefficient of the current time can be obtained, and subsequent real-time monitoring of the current of the electric heat tracing pipe can be realized based on the current fault coefficient.

[0108] Preferably, in an embodiment of the present application, the method for obtaining the current fault coefficient of the current time specifically comprises:

[0109] The signal-to-noise ratio of the current data of the sliding window of the current time is negatively correlated, and the abnormality degree of the current of the sliding window of the current time is obtained.

[0110] The current abnormality degree and the current affected degree of the sliding window of the current time are integrated and normalized, and the calculation result is limited to , so as to obtain the current fault coefficient of the current time.

[0111] In an embodiment of the present application, the sum or product value of the current abnormality degree and the current affected degree of the sliding window of the current time can be calculated to realize the integration of the two, which is not limited herein.

[0112] As an example, in an embodiment of the present application, the expression of the current fault coefficient of the current time can be specifically, for example:

[0113]

[0114] wherein, represents a current time instant current fault coefficient; represents a current time instant current data signal-to-noise ratio of a sliding window; represents a current time instant current abnormality degree of a sliding window; represents a current time instant current affected degree of a sliding window; represents a normalization function for normalization processing; represents a preset second adjustment parameter for preventing a denominator from being 0, a value range of In an embodiment of the present application, the is set to 0.01, a specific value of the can also be set by an implementer according to a specific implementation scenario, which is not limited herein.

[0115] It should be noted that in other embodiments of the present application, a negative correlation mapping can also be realized by other basic mathematical operations, which is not described herein.

[0116] Step S4: Real-time fault monitoring is performed on the current of the electric heat tracing pipe based on the current time instant current fault coefficient.

[0117] The above process realizes real-time analysis of the current fault of the self-limiting temperature electric heat tracing pipe, and then real-time fault monitoring is performed on the current of the electric heat tracing pipe based on the current time instant current fault coefficient, thereby improving the accuracy of the current fault monitoring of the self-limiting temperature electric heat tracing pipe.

[0118] Preferably, in an embodiment of the present application, the method for real-time fault monitoring on the current of the electric heat tracing pipe specifically comprises:

[0119] If the current time instant current fault coefficient is not greater than a preset first fault threshold, the current time instant current fault type is set to a normal fluctuation type, if the current time instant current fault coefficient is greater than the preset first fault threshold and not greater than a preset second fault threshold, the current time instant current fault type is set to a current overload type, and if the current time instant current fault coefficient is greater than the preset second fault threshold, the current time instant current fault type is set to a short circuit or an open circuit, wherein a value range of the preset first fault threshold is , and a value range of the preset second fault threshold is In an embodiment of the present application, the preset first fault threshold is set to 0.4, and the preset second fault threshold is set to 0.8. The preset first fault threshold and the preset second fault threshold can also be set by an implementer according to a specific implementation scenario, which is not limited herein.

[0120] When any one of the current fault types is monitored for a single time, a log is recorded, when the current overload type or short circuit or open circuit is monitored for multiple times in succession, an alarm information is triggered, in an embodiment of the present application, when the current overload type or short circuit or open circuit is monitored for three times in succession, an alarm information is triggered, when the short circuit or open circuit is monitored for more than a preset time length in succession, the power is cut off, in an embodiment of the present application, the preset time length is set to 5 seconds, and the specific value of the preset time length can also be set by the implementer according to the specific implementation scene, which is not limited here.

[0121] An embodiment of the present application provides a self-limiting temperature electric heat tracing band heating current monitoring system, the system comprises a memory, a processor and a computer program, wherein the memory is used for storing the corresponding computer program, the processor is used for running the corresponding computer program, and the computer program can realize the method described in steps S1-S4 when running in the processor.

[0122] It should be noted that the above-mentioned embodiment sequence of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or can be advantageous.

[0123] Each of the embodiments in the specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. A self-limiting temperature limited electric tracing band heating current monitoring method, characterized by, The method comprises: obtaining current data, surface temperature data and ambient temperature data of the electric heat tracing band during operation; constructing a sliding window at each time according to the sampling frequency of the current data and the local distribution of the current data at each time; wavelet decomposing the current data in the sliding window at each time according to the fluctuation of the current data in the sliding window at each time and the signal-to-noise ratio, to obtain current component data of the sliding window at each time; clustering the current component data in the sliding window at all times according to the difference between the current component data of the sliding window at each time and the surface temperature data, and the difference between the current component data of the sliding window at each time and the ambient temperature data, to obtain a plurality of first clustering clusters and a plurality of second clustering clusters; obtaining the current affected degree of the sliding window at the current time according to the distribution of each current component data of the sliding window at the current time in the first clustering cluster and the second clustering cluster; obtaining the current fault coefficient of the current at the current time according to the signal-to-noise ratio of the current data of the sliding window at the current time and the current affected degree; based on the current fault coefficient at the current time, real-time fault monitoring is performed on the current of the electric heat tracing band; the obtaining of the plurality of first clustering clusters and the plurality of second clustering clusters comprises: using a dynamic time warping algorithm to process each of the current component data of the sliding window at each time and the surface temperature data of the sliding window at each time, to obtain a first minimum cumulative distance of each current component data in the sliding window at each time; for all current component data in the sliding window at all times, taking the absolute value of the difference between the first minimum cumulative distances of any two current component data as the first distance measurement between any two current component data, and clustering all the current component data in the sliding window at all times to obtain a plurality of first clustering clusters; using a dynamic time warping algorithm to process each of the current component data of the sliding window at each time and the ambient temperature data of the sliding window at each time, to obtain a second minimum cumulative distance of each current component data in the sliding window at each time; for all current component data in the sliding window at all times, taking the absolute value of the difference between the second minimum cumulative distances of any two current component data as the second distance measurement between any two current component data, and clustering all the current component data in the sliding window at all times to obtain a plurality of second clustering clusters.

2. A self-limiting temperature limited electric tracing band current monitoring method according to claim 1, wherein, the construction of the sliding window at each time comprises: taking the integral of the product of a preset first coefficient and the sampling frequency of the current data to obtain a basic length at each time; taking the average value of all current data in the basic window at each time as the local current level at each time, wherein the length of the basic window is equal to the basic length; taking the product of a preset second coefficient and the rated current of the electric heat tracing band as a current judgment threshold value, and when the local current level at each time is not greater than the current judgment threshold value, taking the basic window at each time as the sliding window at each time; When the local current level at each moment is greater than the current judgment threshold, the length of a basic window at each moment is reduced to obtain a sliding window at each moment.

3. A self-limiting temperature limited electric tracing band current monitoring method according to claim 1, wherein, The obtaining of the current component data of the sliding window at each moment comprises: According to the fluctuation of the current data in the sliding window at each moment, a wavelet basis is selected for the sliding window at each moment; According to the signal-to-noise ratio of the current data of the sliding window at each moment, a decomposition layer number of the sliding window at each moment is obtained; Based on the wavelet basis and the decomposition layer number of the sliding window at each moment, the current data in the sliding window at each moment is wavelet-decomposed, and the low-frequency component and the high-frequency component obtained by the decomposition are taken as the current component data of the sliding window at each moment.

4. A self-limiting temperature limited electric tracing band current monitoring method according to claim 3, wherein, The selecting of the wavelet basis for the sliding window at each moment comprises: The Fourier transform is performed on the current data in the sliding window at each moment to obtain a plurality of frequency components in the sliding window at each moment, and the high-frequency components are screened out from all the frequency components in the sliding window at each moment, the Parseval theorem is used, and based on all the high-frequency components in the sliding window at each moment, the high-frequency component energy of the sliding window at each moment is obtained; Any moment is taken as a target moment, and based on a wavelet selection factor calculation formula, a wavelet selection factor of the sliding window at the target moment is obtained, the wavelet selection factor calculation formula is: in, The wavelet selection factor representing the sliding window of the target time; represents the kurtosis of the current data in the sliding window at the target time; represents the standard deviation of the current data in the sliding window at the target time; Relative coefficient of change of the sliding window representing the target moment; Represents the high-frequency component energy of the sliding window at the target moment; The center point of the sliding window representing the target time and the The distance between the center points of the sliding windows at other moments; Indicates the time other than the target time The high-frequency component energy of the sliding window at other moments; Expressed as a natural constant An exponential function with base ; represents the normalization function; Indicates preset adjustment parameters; If the wavelet selection factor of the sliding window at the target moment is greater than a preset selection threshold, the Haar wavelet is selected as the wavelet basis of the sliding window at the target moment, and if the wavelet selection factor of the sliding window at the target moment is not greater than the preset selection threshold, the Symlet wavelet is selected as the wavelet basis of the sliding window at the target moment.

5. A self-limiting temperature limited electric tracing band current monitoring method according to claim 3, wherein, The obtaining of the decomposition layer number of the sliding window at each moment comprises: The signal-to-noise ratios of the current data of the sliding windows at all moments are divided into two categories using a support vector machine, and the average value of all the signal-to-noise ratios in each category is taken as the overall signal-to-noise ratio of each category; The decomposition layer number of the sliding window at each moment corresponding to the signal-to-noise ratio in the category of the maximum overall signal-to-noise ratio is set as a preset first layer number; The decomposition layer number of the sliding window at each moment corresponding to the signal-to-noise ratio in the category of the minimum overall signal-to-noise ratio is set as a preset second layer number, and the preset first layer number is less than the preset second layer number.

6. A self-limiting temperature limited electric tracing band current monitoring method according to claim 1, wherein, The obtaining of the current current affected degree of the sliding window at the current moment comprises: The average value of the first minimum cumulative distances of all the current component data of the sliding window at the current moment is taken as the first overall cumulative distance of the sliding window at the current moment. averaging the first minimum cumulative distance of all current component data in each first clustering cluster as a first clustering center value of each first clustering cluster, taking the absolute value of the difference between the first minimum cumulative distance of any current component data in the sliding window of the current moment and the first clustering center value of the first clustering cluster where the current component data is located as a first distance parameter of the current component data, and averaging the first distance parameters of all current component data in the sliding window of the current moment as a first overall distance parameter of the sliding window of the current moment; averaging the second minimum cumulative distance of all current component data in the sliding window of the current moment as a second overall cumulative distance of the sliding window of the current moment; averaging the second minimum cumulative distance of all current component data in each second clustering cluster as a second clustering center value of each second clustering cluster, taking the absolute value of the difference between the second minimum cumulative distance of any current component data in the sliding window of the current moment and the second clustering center value of the second clustering cluster where the current component data is located as a second distance parameter of the current component data, and averaging the second distance parameters of all current component data in the sliding window of the current moment as a second overall distance parameter of the sliding window of the current moment; obtaining the current moment current fault coefficient based on the current affected degree calculation formula, wherein the current affected degree calculation formula is: wherein, represents a degree of influence on the current of the sliding window at the current time point; represents a first overall accumulated distance of the sliding window at the current time point; represents a first overall distance parameter of the sliding window at the current time point; represents a second overall accumulated distance of the sliding window at the current time point; represents a second overall distance parameter of the sliding window at the current time point.

7. A self-limiting temperature limited electric tracing band current monitoring method according to claim 1, wherein, the obtaining of the current moment current fault coefficient includes: performing negative correlation mapping on the signal-to-noise ratio of the current data of the sliding window of the current moment to obtain the current moment current abnormality degree; performing comprehensive processing on the current abnormality degree and the current affected degree of the sliding window of the current moment and performing normalization processing to obtain the current moment current fault coefficient.

8. A self-limiting temperature limited electric tracing band current monitoring method according to claim 1, wherein, the real-time fault monitoring of the current of the electric tracing band includes: if the current moment current fault coefficient is not greater than a preset first fault threshold, setting the current moment current fault type as a normal fluctuation type, if the current moment current fault coefficient is greater than the preset first fault threshold and not greater than a preset second fault threshold, setting the current moment current fault type as a current overload type, and if the current moment current fault coefficient is greater than the preset second fault threshold, setting the current moment current fault type as a short circuit or an open circuit; when any one of the current fault types is monitored at a single time, a log is recorded, when the current overload type or the short circuit or the open circuit is monitored continuously for multiple times, an alarm information is triggered, and when the short circuit or the open circuit is continuously monitored for more than a preset time length, the power supply is cut off.

9. A self-limiting temperature electric tracing tape heating current monitoring system, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, the processor executes the computer program to realize the steps of the method in any one of claims 1-8. the processor executes the computer program to realize the steps of the method in any one of claims 1-8.

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